Self-adaptive block-based compressed sensing imaging for remote sensing applications

In order to effectively alleviate the pressure of high-resolution imaging and massive data storage and transmission, it is of great practical significance to introduce compressed sensing into remote sensing applications. From the perspective of imaging control strategy, the typical block-based compressed sensing (BCS) system is optimized. Based on the fact that there are generally significant differences between regions of remote sensing images, a self-adaptive BCS method is proposed. Compared with the traditional BCS system, the prior information of the imaging target is obtained first by adding a presampling process. On the one hand, it is used to generate a saliency information map, which guides the reasonable allocation of self-adaptive sampling ratios between blocks in the compressed sampling process, thereby improving the sampling efficiency. On the other hand, it is used to generate the weighted sparse coefficient matrix, which will be substituted into the theoretical model in the image restoration process, thus improving the image restoration efficiency. The experimental results show that the imaging quality of the proposed method has a significant improvement compared with the traditional system and is also superior to several existing self-adaptive methods. In addition, on the basis of the above method, a multiangle image restoration strategy is proposed, which further improves the image quality at the cost of four times the image restoration time. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).